Journal article
Real-world benchmarking and validation of foundation model transformers for endometrial cancer subtyping from histopathology
NPJ precision oncology
04/04/2026
DOI: 10.1038/s41698-026-01402-4
PMID: 41935172
Abstract
We benchmarked histopathology foundation encoders paired with attention-based multiple instance learning (MIL) against convolutional neural networks (CNNs) to assess their robustness for endometrial cancer molecular classification (MMR-deficient, p53 aberrant, POLE pathogenic mutation, and no specific molecular profile) from whole-slide images (WSIs) in a real-world cohort. A public cohort of 815 patients (1195 WSIs) was assembled for model development. Generalizability was evaluated using an external cohort of 720 patients (1357 WSIs). Models were trained using five-fold cross-validation and tested on the external cohort. Performance was summarized using macro-area under the receiver operating characteristic curve (AUC), macro-F1 score, and balanced accuracy. In cross-validation, foundation encoder models outperformed CNNs (macro-AUC 0.799-0.860 vs 0.715-0.829). The best configuration (Virchow2 with CLAM MIL) achieved macro-AUC 0.860, macro-F1 score 0.607, and balanced accuracy 0.647. On external validation, CNN performance degraded substantially, whereas foundation models retained higher discrimination. UNI2 with CLAM MIL achieved the highest external macro-AUC 0.780 with a macro-F1 score of 0.416 and balanced accuracy of 0.507. Subtype-level performance was highest for p53abn (AUC 0.851). When evaluated within a benchmarking framework, foundation encoders paired with attention-based MIL demonstrate improved generalization for endometrial cancer molecular subtyping from WSIs compared with CNNs, supporting their potential for subtype inference.
Details
- Title: Subtitle
- Real-world benchmarking and validation of foundation model transformers for endometrial cancer subtyping from histopathology
- Creators
- Vincent M Wagner - University of IowaCasey M Cosgrove - The Ohio State University Comprehensive Cancer Center – Arthur G. James Cancer Hospital and Richard J. Solove Research InstituteStephanie J Chen - University of IowaDaniel T Griffin - University of IowaMegan I Samuelson - University of IowaMichael J Goodheart - University of IowaJesus Gonzalez-Bosquet - University of Iowa, Obstetrics and Gynecology
- Resource Type
- Journal article
- Publication Details
- NPJ precision oncology
- DOI
- 10.1038/s41698-026-01402-4
- PMID
- 41935172
- NLM abbreviation
- NPJ Precis Oncol
- ISSN
- 2397-768X
- eISSN
- 2397-768X
- Publisher
- Springer Nature
- Grant note
- K12TR004382 / National Center for Advancing Translational Sciences of the National Institute of Health (The Institute for Clinical and Translational Science at the University of Iowa K12 Award Program)
- Language
- English
- Electronic publication date
- 04/04/2026
- Academic Unit
- Pathology; Obstetrics and Gynecology
- Record Identifier
- 9985151594202771
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